Optimizing personalised recommender systems for teachers’ digital learning models using deep learning algorithms

Bibliographic Data

ID21631283
AuthorsDeming Li (0000-0001-7145-9263, Jilin International Studies University, corresponding author), Kellyt D Ortegas (Merced College)
Year2026
Volume34
Issue3
Pages1717-1731
Publication date2026-03-16
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueInteractive Learning Environments (JOURNAL)
Journal identifiersISSN: 1049-4820 • E-ISSN: 1744-5191
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10494820.2025.2528104
OpenAlexW4413533454
LanguageEN
References cited21

Digital Learning Models for Teachers is an important educational development to improve teaching effectiveness through digital technology. It creates learning experiences with personalization and customization by providing teachers with more accurate feedback on their teaching. However, the current problem is that the learning question types must be recommended according to the students’ needs, leading to inadequate learning effects. To solve the problem, this paper proposed a personalized recommendation algorithm based on a graph neural network for teachers’ digital learning models (PRAGNN). In particular, firstly, integrating DINA cognitive diagnosis and gray partial correlation evaluation is used to construct a student model by modeling students’ mastery of knowledge points and cognitive ability level. Secondly, a graph convolutional neural network is introduced and combined with the sequential relationship between subject knowledge points to automatically capture the semantic information of higher-order structures in the knowledge points to achieve personalized recommendations. Ultimately, the algorithm achieved 85.60% accuracy through comparative experiments. This research provides a new idea for constructing a personalized recommendation system for the digital learning model of teachers, it has significant value for the sustainable development of lifelong learning that promotes mutual growth between teachers and students

Algorithm · Collaborative filtering · Educational technology · Machine learning · Mathematics education · Multimedia · Recommender system · Cloud Computing and Resource Management · Computer Science · Mathematics · Online Learning and Analytics · Recommender Systems and Techniques · Artificial Intelligence

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